Create CLIP_model.py

CLIP_model.py

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+import torch
+from torch import nn
+import torch.nn.functional as F
+from torchvision import models, transforms
+from transformers import AutoTokenizer, AutoModel
+import config as CFG
+import cv2
+
+class CLIPModel(nn.Module):
+    """CLIP model for Bangla"""
+    def __init__(self):
+        super(CLIPModel, self).__init__()
+        self.image_encoder = models.efficientnet_b2(weights = "EfficientNet_B2_Weights.DEFAULT")
+        self.image_encoder.fc = nn.Identity()
+        
+        self.image_out = nn.Sequential(
+            nn.Linear(CFG.image_embedding, 256), nn.ReLU(), nn.Linear(256, 256)
+        )
+
+        self.text_encoder = AutoModel.from_pretrained(CFG.text_encoder_model)
+        self.target_token_idx = 0
+
+
+        self.text_out = nn.Sequential(
+            nn.Linear(768, 256), nn.ReLU(), nn.Linear(256, 256)
+        )
+      
+
+    def forward(self, image, text, mask):
+        image_vec = self.image_encoder(image)        
+        image_vec = self.image_out(image_vec)
+
+        text_out = self.text_encoder(text, mask)
+        last_hidden_states = text_out.last_hidden_state
+
+        last_hidden_states = last_hidden_states[:,self.target_token_idx,:]
+        text_vec = self.text_out(last_hidden_states.view(-1,768))
+
+        return image_vec, text_vec
+    
+    def get_image_embeddings(self, image):
+        image_vec = self.image_encoder(image)        
+        image_vec = self.image_out(image_vec)
+
+        return image_vec
+    
+    def get_text_embeddings(self, text, mask):
+        text_out = self.text_encoder(text, mask)
+        last_hidden_states = text_out.last_hidden_state
+
+        last_hidden_states = last_hidden_states[:,self.target_token_idx,:]
+        text_vec = self.text_out(last_hidden_states.view(-1,768))
+
+        return text_vec
+
+    
+
+if __name__ == '__main__':
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    images = torch.randn(40, 3, 224, 224).to(device)
+    input_ids = torch.randint(5, 300, size=(40, 200)).to(device)
+    attention_mask = torch.ones(40, 200).to(device)
+
+    print("Building CLIP")
+    clip_model = CLIPModel().to(device)
+    print(clip_model)
+
+    img_vec, text_vec = clip_model(images, input_ids, attention_mask)
+    print(img_vec.shape)
+    print(text_vec.shape)